TFWT: Tabular Feature Weighting with Transformer
This addresses the problem of suboptimal performance in tabular data analysis for machine learning practitioners, though it appears incremental as it builds on existing Transformer and reinforcement learning techniques.
The paper tackles the limitation of existing feature processing methods for tabular data, which assume equal importance across features, by proposing TFWT, a novel feature weighting method using Transformer and reinforcement learning; experimental results across real-world datasets demonstrate its effectiveness in enhancing performance.
In this paper, we propose a novel feature weighting method to address the limitation of existing feature processing methods for tabular data. Typically the existing methods assume equal importance across all samples and features in one dataset. This simplified processing methods overlook the unique contributions of each feature, and thus may miss important feature information. As a result, it leads to suboptimal performance in complex datasets with rich features. To address this problem, we introduce Tabular Feature Weighting with Transformer, a novel feature weighting approach for tabular data. Our method adopts Transformer to capture complex feature dependencies and contextually assign appropriate weights to discrete and continuous features. Besides, we employ a reinforcement learning strategy to further fine-tune the weighting process. Our extensive experimental results across various real-world datasets and diverse downstream tasks show the effectiveness of TFWT and highlight the potential for enhancing feature weighting in tabular data analysis.